Separating Hate Speech and Offensive Language Classes via Adversarial Debiasing

Research to tackle hate speech plaguing online media has made strides in providing solutions, analyzing bias and curating data. A challenging problem is ambiguity between hate speech and offensive language, causing low performance both overall and specifically for the hate speech class. It can be argued that misclassifying actual hate speech content as merely offensive can lead to further harm against targeted groups. In our work, we mitigate this potentially harmful phenomenon by proposing an adversarial debiasing method to separate the two classes. We show that our method works for English, Arabic German and Hindi, plus in a multilingual setting, improving performance over baselines

Portrayal of fuzzy recharge areas for water balance modelling – a case study in northern Oman

The research project IWAS Oman aims at implementing integrated water resources management (IWRM) to a pilot area in Al Batinah, Oman. This requires – amongst others – a realistic assessment of groundwater recharge to the alluvial aquifer which obviously has to be based upon the extension of recharge areas. In this context, the subsequent investigation focuses on the role of vagueness as regards the portrayal of the areas that provide water for particular aquifers. For that purpose, concepts of fuzziness in spatial analysis are applied to describe possible extents of recharge areas. <br><br> In general, any water assessment is based on clearly delineated boundaries. However, in many cases, aquifer recharge areas are not clearly defined due to the nature of the study area. Hence, surfaces indicating a gradual membership to the recharge area of a particular aquifer are used in this investigation. These surfaces, which are based on available qualitative information, visualise a potential range of spatial extension. With regard to water balance calculations, functional relationships in tabular form are derived as well. Based on a regionalisation approach providing spatially distributed recharge rates, the corresponding recharge volume is calculated. Hence, this methodology provides fuzzy input data for water balance calculations. Beyond the portrayal of one singular aquifer recharge area, this approach also supports the complementary consideration of adjacent areas

Лазерное инициирование порошков тэна в условиях объемного сжатия

Определены энергетические пороги инициирования и исследована кинетика процесса взрывного разложения порошков тетранитрата пентаэритрита, объемносжатых до давления 5·108Н/м2, при воздействии импульсом лазерного излучения на длинах волн 1064 нм (область прозрачности) и 266 нм (область собственного поглощения). Реализованы условия низкопорогового инициирования для порошков чистого тэна первой, второй и четвертой гармониках излучения неодимового лазера

Development of a PIGE-Detection System for in-situ Inspection and Quality Assurance in the Evolution of Fast Rotating Parts in High Temperature Environment Manufactured from TiAl

Intermetallic γ-titanium aluminides are a promising material in high temperature technologies. Their high specific strength at temperatures above 700°C offers the possibility for their use as components of aerospace and automotive industries. With a specific weight of 50% of that of the widely used Ni-based superalloys TiAl is very suitable as material for fast rotating parts like turbine blades in aircraft engines and land based power stations or turbocharger rotors. Thus lower mechanical stresses and a reduced fuel consumption and CO2-emission are expected. To overcome the insufficient oxidation protection the halogen effect offers an innovative way. After surface doping using F-implantation or liquid phase-treatment with an F-containing solution and subsequent oxidation at high temperatures the formation of a protective alumina scale can be achieved. By using non-destructive ion beam analyses (PIGE, RBS) F was found at the metal/oxide interface. For analysis of large scale components a new vacuum chamber at the IKF was installed and became operative. With this prototype of in-situ quality assurance system for the F-doping of manufactured parts from TiAl some performance test measurements were done and presented in this paper.Received: 01 March 2013; Revised: 24 April 2013; Accepted: 25 April 201

Development of a PIGE-Detection System for In-situ Inspection and Quality Assurance in the Evolution of Fast Rotating Parts in High Temperature Environment Manufactured From TiAl

Intermetallic γ-titanium aluminides are a promising material in high temperature technologies. Their high specific strength at temperatures above 700°C offers the possibility for their use as components of aerospace and automotive industries. With a specific weight of 50% of that of the widely used Ni-based superalloys TiAl is very suitable as material for fast rotating parts like turbine blades in aircraft engines and land based power stations or turbocharger rotors. Thus lower mechanical stresses and a reduced fuel consumption and CO2-emission are expected. To overcome the insufficient oxidation protection the halogen effect offers an innovative way. After surface doping using F-implantation or liquid phase-treatment with an F-containing solution and subsequent oxidation at high temperatures the formation of a protective alumina scale can be achieved. By using non-destructive ion beam analyses (PIGE, RBS) F was found at the metal/oxide interface. For analysis of large scale components a new vacuum chamber at the IKF was installed and became operative. With this prototype of in-situ quality assurance system for the F-doping of manufactured parts from TiAl some performance test measurements were done and presented in this paper.Received: 01 March 2013; Revised: 24 April 2013; Accepted: 25 April 201

Basal forebrain integrity and cognitive memory profile in healthy aging

Age-related dysfunctions in cholinergic and dopaminergic neuromodulation are assumed to contribute to age-associated impairment of explicit memory. Both neurotransmitters also modulate attention, working memory, and processing speed. To date, in vivo evidence linking structural age-related changes in these neuromodulatory systems to dysfunction within or across these cognitive domains remains scarce. Using a factor analytical approach in a cross-sectional study including 86 healthy older (aged 55 to 83 years) and 24 young (aged 18 to 30 years) adults, we assessed the relationship between structural integrity-as measured by magnetization transfer ratio (MTR)-of the substantia nigra/ventral tegmental area (SN/VTA), main origin of dopaminergic projections, basal forebrain (major origin of cortical cholinergic projections), frontal white matter (FWM), and hippocampus to neuro psychological and psychosocial scores. Basal forebrain MTR and FWM changes correlated with a factor combining verbal learning and memory and working memory and, as indicated by measures of diffusion, were most likely due to vascular pathology. These findings suggest that frontal white matter integrity and cholinergic neuromodulation provide clues as to why age-related cognitive decline is often correlated across cognitive domains. (C) 2009 Elsevier B.V. All rights reserved

Efficient determination of cysteine sulphoxides in Allium plants applying new biosensor and HPLC-MS² methods

Cysteine sulphoxide (CSO) contents of 16 different accessions of garlic (Allium sativum L.) and 15 varieties of onion (Allium cepa L.) were measured using two different rapid analytical methods: a biosensoric approach and a newly developed HPLC-MS2 technique. Both methods allow quantification of naturally occurring cysteine sulphoxides present in Allium plants without time-consuming sample pretreatment such as derivatisation of amino acid derivatives prior to HPLC-separation. It has been found that the amount of alliin, which is the predominant CSO occurring in garlic, varies between 0.2 and 2.2 g/100 g dry matter. Contrary to that, isoalliin representing the main CSO in onion has been detected in significantly lower amounts (0.3 to 1.25 g/100 g dry matter)

Identifying automatically generated headlines using transformers

False information spread via the internet and social media influences public opinion and user activity, while generative models enable fake content to be generated faster and more cheaply than had previously been possible. In the not so distant future, identifying fake content generated by deep learning models will play a key role in protecting users from misinformation. To this end, a dataset containing human and computer-generated headlines was created and a user study indicated that humans were only able to identify the fake headlines in 47.8% of the cases. However, the most accurate automatic approach, transformers, achieved an overall accuracy of 85.7%, indicating that content generated from language models can be filtered out accurately